We propose Graph-to-Kernel LSTM (G2K LSTM) that combines the spatial physical boundaries and the visual attention scope to shape each pedestrian neighborhood. G2K LSTM is an LSTM-based approach that transforms a spatio-temporal graph into the kernel to estimate the correlations between pedestrians. This correlation represents the importance of a relationship between two people, stemming from the natural correlation between their visual angle and their relative distances. In sociological and psychological proxemics [1]}, [2]}, human maintains specific distance from others to be his/her personal zone within which they feel comfortable and evade collisions. This zone is maintained by what they observe and pay attention to.
